The effect of investor sentiment on returns of JSE size-based indices under changing market conditions: Evidence from a Markov regime-switching model

The objective of the study is to examine the effects of investor sentiment on the Johannesburg Stock Exchange (JSE) index returns in bull and bear market conditions. Accordingly, this study uses monthly data to construct a new market-wide investor sentiment index and test its effects on the JSE aggregated and disaggregated index returns in alternating market conditions for the period March 2007 to January 2024. The findings of the Markov regime-switching model reveal that when the JSE is in a bull market condition, the JSE oil and gas sector returns and the JSE telecommunication sector returns are affected positively by investor sentiment. Similarly, in a bearish state, the JSE health sector returns and JSE telecommunication sector returns are negatively affected by investor sentiment. Collectively, the findings suggest that the effects of investor sentiment on JSE index returns are regime-specific and time-varying, such that they are dependent on the market conditions (bull or bear) and the type of JSE index (aggregated or disaggregated index). Accordingly, investors must consider this information to ensure resilient investment decisions and risk management strategies in sentiment-induced markets and alternating market conditions.

The excess levels of investor participation coupled with irrational behaviour in the South African bond market causes excess volatility, which in turn exposes investors to losses. Consequently, the study aims to examine the effect of market-wide investor sentiment on government bond index returns of varying maturities under changing market conditions. This study constructs a new market-wide investor sentiment index for South Africa and uses the two-state Markov regime-switching model for the sample period 2007/03 to 2024/01. The findings illustrate that the effect investor sentiment has on government bond indices returns of varying maturities is regime-specific and time-varying. For instance, the 1–3-year government index return and the over-12-year government bond index were negatively affected by investor sentiment in a bull market condition and not in a bear market condition. Moreover, the bullish market condition prevailed among the returns of selected government bond indices of varying maturities. The findings suggest that the government bond market is adaptive, as proposed by AMH, and contains alternating efficiencies. The study contributes to the emerging market literature, which is limited. That being said, it uses market-wide investor sentiment as a tool to make pronunciations on asset selection, portfolio formulation, and portfolio diversification, which assists in limiting investor losses. Moreover, the findings of the study contribute to settling the debate surrounding the efficiency of bond markets and the effect between market-wide sentiment and bond index returns in South Africa. That being said, it is nonlinear, which is a better modelled using nonlinear models and alternates with market conditions, making the government bond market adaptive.

The exponential growth in popularity of ETFs over the last three decades has solidified ETFs as an essential component of many investors’ portfolios. Investor sentiment is one of the factors that influence market returns of ETFs during times of market volatility. This article highlights the gap in the literature by examining the role sentiment plays in ETF volatility and providing a more comprehensive understanding of how sentiment interacts with market conditions to affect ETF pricing in the South African context. This article aims to determine the effect of investor sentiment on JSE-listed ETF returns under changing market conditions. The study followed a quantitative methodology using monthly closing prices of seven JSE ETFs and an investor sentiment index. A sample period from October 2008 to December 2023 was used. For a more complex understanding of how sentiment evolved and influenced market regimes, the Markov regime-switching model was integrated with Principal Component Analysis. The results found that investor sentiment had a significant impact on most of the ETFs in both the bull and bear regimes. The bull market was more dominant than the bear market across the ETF returns. Therefore, investor sentiment affected the returns of JSE ETFs. Identifying the effect of investor sentiment on ETFs results in ETF portfolios being less affected by changing market conditions by using risk management techniques and diversifying across asset classes and investing methods.

While prior studies have examined the predictive effect of macroeconomic and country risk components on property stock index dynamics, limited explanations exist in the literature regarding the time-varying effect of investor sentiment on housing prices. Accordingly, this study assesses the impact of investor sentiment on housing properties’ returns and the effect of investor sentiment on the conditional volatility of housing price indices under different market conditions, using GARCH, GJR-GARCH, E-GARCH and Markov-switching VAR models. We found investor sentiment to significantly impact the risk premium of the property returns, where property returns increased with positive changes in investor sentiment, and conditional volatility of property returns decreased with the same changes in investor sentiment. Investor sentiment exerts positive predictive influences on the prices of small and medium houses, in both bullish and bearish market conditions but does not affect the large housing market segment. This makes the implementation of risk-related diversification across small and medium real estate portfolios more effective than large real estate portfolios. Our findings show that investor sentiment is a plausible driver of mass investor redemption actions under conditions of uncertainty.

Information asymmetry, sentiment interactions, and asset price

Existing research focuses on buy-write strategy performance when index options are used as the underlying asset, finding positive excess risk-adjusted returns which are suggestive of option overpricing. My purpose is to extend this literature by conducting a thorough analysis of strategy performance when individual stock options are used instead of index options. Moreover, I examine whether underlying asset class and investor sentiment has an effect on buy-write performance. Using US data from 2008 - 2015, I sort S&P 500 constituents to form portfolios of large, small, growth and value stocks and test for differences in buy-write performance. The returns of each portfolio are then regressed against 2 separate proxies of investor sentiment and several control variables to test the effects of investor sentiment. Contrary to aforementioned buy-write research, I find no evidence of excess risk-adjusted returns, likely due to the implied vs. realised volatility anomaly which is observed in index options but not stock options. Despite existing evidence that options on small and value stocks are expensive relative to large and growth stocks, I find no evidence that firm characteristic has an effect on buy-write performance. This is potentially explained by the relative illiquidity of small and value options resulting in increased trading costs which are not accounted for in previous studies. Consistent with the literature, my results show that in general, investor sentiment has a positive relationship with buy-write returns, especially for small and value stocks. Additional sub-sample analysis shows that during a market downturn the effect of investor sentiment is much stronger, likely due the limited ability of arbitrageurs to exploit mispriced securities. During times of low market volatility the effect of investor sentiment becomes lagged and much weaker in magnitude.

Meticulous empirical research remains to determine whether the Adaptive Markets Hypothesis (AMH) or the more widely known Efficient Market Hypothesis (EMH) better explains investor overconfidence and stock return volatility behaviour. Investor overconfidence is vital in understanding why investment strategies are pursued so aggressively, leading to excessive market trading. It is often argued that the investor overconfidence bias makes markets less efficient because it creates pricing errors in extreme volatility and overestimates investors’ beliefs in the accuracy of their forecasts of their quotes on prices. This research analyses the effect of investor overconfidence on the volatility of stock market returns according to the AMH in seven African stock markets, including the Casablanca Stock Exchange, the Egyptian Exchange, the Johannesburg Stock Exchange, the Nigerian Stock Exchange, the Nairobi Stock Exchange, the Ghana Stock Exchange, and the Stock Exchange of Mauritius. The sample period includes secondary time series data from January 2005 to December 2019. The first goal was to develop and validate a measure of investor overconfidence. The second objective was to compare different levels of investor overconfidence in the selected African stock markets. The third objective was to evaluate the influence of investor overconfidence on the volatility of stock market return under changing market conditions, as described by the AMH. The estimation methods included the Generalised Methods of Moments dummy regression, regime-switching VAR models and rolling GARCH models, which are GARCH, EGARCH and TARCH. The results show that high investor overconfidence is more associated with bullish markets than periods of financial crises and bearish markets. The results also imply that it is not advisable to generalise the impact of market conditions on investor overconfidence across all the markets. Additionally, rolling GARCH estimates demonstrated that patterns of investor overconfidence evolve, consistent with the AMH. Assessing investor overconfidence under the AMH framework offers a stronger image of the adaptive behaviour of the Afri can equity markets. This research adds to existing knowledge in numerous ways. Foremost, it provides a standard measure of investor overconfidence in Africa’s equity markets. A measure that combines multiple proxies into a single index and neutralizes the disadvantages of each proxy when used separately to estimate investor overconfidence. Second, it provides a timely contribution to the effect of investor overconfidence on stock return volatility in African equity markets under the AMH paradigm. Third, according to the AMH, investor confidence is not vi static and can appear under specific market conditions and disappear under others. This bias occurs and disappears as market conditions change in the chosen African equity exchanges. This also shows that investor overconfidence is normal, changes over time and is adaptable in the African stock markets. Consequently, this study brings a new perspective regarding investor overconfidence and market efficiency in the face of the AMH paradigm. The results also have important implications for investors and brokers wishing to develop appropriate trading strategies. This study is also helpful for policymakers as they need to be wary about investor overconfidence impact on market momentum in periods of market expansion. This study argues that investor overconfidence in African stock markets conforms to the AMH than the EMH and the BF. Iqoqa. Ukuzethemba ngokweqile kubatshalizimali kubalulekile ekuqondeni ukuthi kungani amasu okutshala izimali elandelwa ngonya, okuholela ekuhwebeni okudlulele. Kodwa-ke, ucwaningo olunzulu lobuchwepheshe lusadingeka ukuze kutholwe ukuthi ingabe i-Adaptive Markets Hypothesis ichaza kangcono ukuzethemba ngokweqile kwabatshalizimali kanye nokuziphatha okuguquguqukayo kokubuya kwesitoko. Ucwaningo luhlaziya umthelela wokuzethemba ngokweqile kwabatshalizimali ekushintsheni kokubuyiselwa kwesitoko ngokusho kwe-AMH ezimakethe zamasheya eziyisikhombisa zase-Afrikha: iGibhithe, iGhana, iKenya, iMauritius, iMorocco, iNigeria kanye neNingizimu Afrikha. Isikhathi sesampula sihlanganisa idatha yochungechunge lwesikhathi sesibili kusukela ngoJanuwari 2005 kuya kuDisemba 2019. Umgomo wokuqala bekuwukusungula nokuqinisekisa isilinganiso sokuzethemba ngokweqile kwabatshalizimali. Okwesibili kwakuwukuqhathanisa amazinga ahlukene okuzethemba ngokweqile kwabatshalizimali ezimakethe zamasheya ezikhethiwe zase-Afrikha. Okwesithathu kwakuwukuhlola umthelela wokuzethemba ngokweqile kwabatshalizimali ekuguquguqukeni kokubuya kwesitoko ngaphansi kwezimo ezishintshayo zezimakethe. Izindlela zokulinganisa zazihlanganisa ukuhlehla kwesifanekiso se-GMM, i-MS-VAR nemodeli ye-GARCH. Imiphumela ibonisa ukuthi ukuzethemba ngokweqile kwabatshalizimali kuhlotshaniswa kakhulu nobundlovu iyangena bezimakethe kunezikhathi zezinkinga zezimali kanye nezimakethe ezisimamayo. Imiphumela iphinde isho ukuthi makungelulekwa ukwenza ngokujwayelekile umthelela wezimo zemakethe ekuzethembeni ngokweqile kwabatshalizimali ezimakethe ezahlukene. Izilinganiso ze-GARCH ephenduphendukayo zibonise ukuvela kwamaphethini okuzethemba ngokweqile kwabatshalizimali, ngokuhambisana ne-AMH. Ukuhlola ukuzethemba ngokweqile kwabatshalizimali ngaphansi kohlaka lwe-AMH kunikeza isithombe esinamandla sokuziphatha okuguquguqukayo kwezimakethe zase-Afrikha. Lolu cwaningo lwengeza olwazini olukhona ngokuqala, lunikeze isilinganiso esijwayelekile sokuzethemba ngokweqile kwabatshalizimali ezimakethe zase-Afrikha. Isilinganiso esihlanganisa abameleli abaningi sibe yinkomba eyodwa futhi sinciphise ubungozi bommeleli ngamunye lapho sisetshenziswa ngokuhlukene ukuze silinganisele ukuzethemba ngokweqile komtshalizimali. Okwesibili, ukunikeza umnikelo ofika ngesikhathi emphumeleni wokuthembela ngokweqile kwabatshalizimali ekuguquguqukeni kokubuya kwesitoko ezimakethe zase-Afrikha. Okwesithathu, ngokusho kwe-AMH, ukuzethemba kwabatshalizimali akumile futhi kungavela ngaphansi kwezimo ezithile zemakethe futhi zinyamalale ngaphansi kwezinye. Imiphumela ibhekisela ekuzethembeni ngokweqile kwabatshalizimali ababhalisela i-AMH kune-EMH noma i-BF. Imiphumela inemithelela ebalulekile kubatshalizimali kanye nabadayisi besitoko abafisa ukuthuthukisa amasu afanele okuhweba. Kuyasiza futhi kubenzi benqubomgomo njengoba kudingeka baqaphele umthelela wokuzethemba ngokweqile kwabatshalizimali kumfutho wemakethe ngezikhathi zokukhula kwemakethe.

Purpose This paper aims to use three proxy variables – initial public offerings, trading volume and business confidence index (BCI) to construct an investor sentiment index both for the USA and China, taking into account the challenging periods of the COVID-19 pandemic and the Russo-Ukrainian conflict. Design/methodology/approach Based on monthly data for a period from January 2009 to June 2022, this paper uses data of BCI, consumer confidence index (CCI), gross domestic product, trading volume and Fama and French (1993) factor data; linear regression of single and multifactor model; and EGARCH-M model for analyzing the effect of investor sentiment on stock market return and volatility, both in the USA and China. Findings The empirical results indicate the suitability of BCI over CCI as a measure of investor sentiment, both in the USA and China. The results indicate that investor sentiment has a significant positive effect on the excess returns in the stock market in both countries. Moreover, the effect of investor sentiment is higher in China than it is in the USA. Such an effect of investor sentiment is significant and fluctuates asymmetrically in the short run but loses its significance in the long run. Optimistic investor sentiment has a larger effect on the stock market volatility in the USA, while the pessimistic investor sentiment has a larger effect in the Chinese stock market. Originality/value This paper focuses on finding a more suitable proxy for investor sentiment from BCI or CCI. This paper also contributes by including both optimism and pessimism in explaining the stock return and volatility in both markets. The overall findings are important for understanding investor behavior in different market conditions.

PurposeInvestor sentiment (optimism or pessimism) may influence investors to follow others (herding) while taking their investment decisions. Herding may result in bubbles and crashes in the financial markets. The purpose of the study is to examine the presence of herding and the effects of investor sentiment on herding in China and Pakistan.Design/methodology/approachThe investor sentiment is captured by five variables (trading volume, advance/decline ratio, weighted price-to-earnings ratio, relative strength index and interest rates) and a sentiment index developed through principal component analysis (PCA). The study uses daily prices of 2,184 firms from China and 568 firms from Pakistan for the period 2005 to 2018.FindingsThe study finds that herding prevails in China while reverse herding prevails in Pakistan. Interestingly, as investors become optimistic, herding in China and reverse herding in Pakistan decrease. This indicates that herding and reverse herding are greater during pessimistic periods. Further, the increase in herding in one market reduces herding in the other market. Moreover, optimistic sentiment in the Chinese market increases herding in the Pakistani market but the reverse is not true.Practical implicationsConsidering the greater global financial liberalization, and better opportunities for emotion sharing, this study has important implications for regulators and investors. Market participants need to understand the prevalent irrational behavior before trading in the markets.Originality/valueSince individual proxies may depict different picture of the relationship between sentiment and herding therefore the study also develops a sentiment index through PCA and incorporates this index in the analysis. Further, this study examines cross-country effects of herding and investor sentiment.

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification.

This study tests for the applicability of the Adaptive Market Hypothesis (AMH) and Bounded rationality theories on the Johannesburg Stock Exchange (JSE). This is in order to determine the influence of behavioural risk factors on the efficiency of the stock market. Behavioural theories show a gap in the theory of Efficient Market Hypothesis. Using quantile regression, our study established the applicability of the Adaptive Market Hypothesis on the JSE. Past market returns were shown to be significant in predicting future returns and thus did not follow a random walk. The lagged return increased at higher quantiles and differed with changes in market conditions (i.e. pre-financial crisis, financial crisis and post-financial crisis). Thus, the predictability of returns varies with a change in market conditions. This paper is focused on only on the Johannesburg stock exchange in South Africa, more specifically the movement is the all share index. The findings should be able to be applied to emerging and developed economies. Business confidence is found to have a negative relationship with returns showing a lag in time for sentiment to be incorporated in prices. In contrast, consumer confidence is found to have a positive relationship with returns. In summary, investors are shown to be influenced by fundamental and behavioural factors.

An analysis of the effect of investor sentiment in a heterogeneous switching transition model for G7 stock markets

This study investigates the role of two prominent concepts in finance: limits to arbitrage and investor sentiment in stock prices. The study examines how changes in market-wide investor sentiment and limits to arbitrage can affect the performance of nine UK stock market anomalies. The extant literature relating to investor sentiment focuses mainly on the US stock market, whilst research on the UK market typically examines aggregated index-level data. In addition, previous studies have focused on examining investor sentiment and limits to arbitrage separately. Using data from UK-listed companies over the period January 1997 to December 2019, the study finds that five stock market anomalies were related to changes in UK investor sentiment and produced significantly higher returns following periods of high investor sentiment, while the effect of limits to arbitrage was mostly limited. However, the interaction analysis provided support to the limits to arbitrage theory and demonstrated that the effect of high investor sentiment on stock market anomalies was more pronounced when combined with high limits to arbitrage and had less effect during periods characterised by low limits to arbitrage.

The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.